IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i10p5964-d815599.html
   My bibliography  Save this article

Prediction of Ozone Hourly Concentrations Based on Machine Learning Technology

Author

Listed:
  • Dong Li

    (College of Economics and Management, Xi’an University of Posts & Telecommunications, Xi’an 710061, China)

  • Xiaofei Ren

    (College of Economics and Management, Xi’an University of Posts & Telecommunications, Xi’an 710061, China)

Abstract

To optimize the accuracy of ozone (O 3 ) concentration prediction, this paper proposes a combined prediction model of O 3 hourly concentration, FC-LsOA-KELM, which integrates multiple machine learning methods. The model has three parts. The first part is the feature construction (FC), which is based on correlation analysis and incorporates time-delay effect analysis to provide a valuable feature set. The second part is the kernel extreme learning machine (KELM), which can establish a complex mapping relationship between feature set and prediction object. The third part is the lioness optimization algorithm (LsOA), which is purposed to find the optimal parameter combination of KELM. Then, we use air pollution data from 11 cities on Fenwei Plain in China from 2 January 2015 to 30 December 2019 to test the validity of FC-LsOA-KELM and compare it with other prediction methods. The experimental results show that FC-LsOA-KELM can obtain better prediction results and has a better performance.

Suggested Citation

  • Dong Li & Xiaofei Ren, 2022. "Prediction of Ozone Hourly Concentrations Based on Machine Learning Technology," Sustainability, MDPI, vol. 14(10), pages 1-29, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5964-:d:815599
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/10/5964/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/10/5964/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cao, Quoc Dung & Miles, Scott B. & Choe, Youngjun, 2022. "Infrastructure recovery curve estimation using Gaussian process regression on expert elicited data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Pavitra Kumar & Sai Hin Lai & Jee Khai Wong & Nuruol Syuhadaa Mohd & Md Rowshon Kamal & Haitham Abdulmohsin Afan & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, vol. 12(11), pages 1-26, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jolanta Katarzyna Grochowska & Renata Augustyniak-Tunowska, 2023. "The Influence of Various Restoration Techniques on the Content of Selected Ions in Water of an Urban Lake," Sustainability, MDPI, vol. 15(16), pages 1-17, August.
    2. Yao, Wen & Zheng, Xiaohu & Zhang, Jun & Wang, Ning & Tang, Guijian, 2023. "Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Wang, Ke & Liu, Jinfeng & Tian, Lai & Tan, Xianfeng & Peng, Guansheng & Qin, Tianwen & Wu, Jun, 2022. "Analyzing vulnerability of optical fiber network considering recoverability," Reliability Engineering and System Safety, Elsevier, vol. 221(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5964-:d:815599. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.